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Sustainable Product Choices Recommendation System Using Contextual Bidirectional Encoder Representations from Transformers and Multi-Layer Perceptron

Sustainable Product Choices Recommendation System Using Cont...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Rana Veer Samara Sihman Bharattej R Haider Alabdeli Tanuja V Dubey D. Beula S meenakshi Sundaram Department of Computer Science Saint Leo University St Leo USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Management Ashoka Business School Mumbai Nashik India Department of Physics New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Recent days, e-commerce platforms have booming tremendously as the people showing interest to buy the products from online based on the customer reviews and ratings. So, providing the best product recommendations to customer will increase global market and GDP growth of a country. The existing methods for recommendation of products had resulted in suggesting unnecessary products to customers. Therefore, in this research Contextual Bidirectional Encoder Representations from Transformers- Multi-Layer Perceptron (CoBERT-MLP) method is proposed for recommending sustainable product choices using a knowledge-based recommender system. The proposed method employed on Flipkart product dataset that provides details of different products. The data obtained from this dataset is pre-processed by utilising the Natural Language Toolkit (NLTK). These pre-processed data features are extracted by the employed CoBERT method that eliminates the irrelevant attributes from the data. These obtained relevant attributes are classified for categorizing the product choices of a customer. Finally, these classified choices are given to knowledge-based recommender system for providing most sustainable product choices to customers. The proposed CoBERT-MLP method achieved better results in terms of accuracy (99.14%), recall (99.38%), precision (98.56%), and F1-score (99.71%) when related with prevailing method Convolutional Neural Networks (CNN).

关键词： Accuracy Reviews Knowledge based systems Natural languages Symbols Feature extraction Transformers Convolutional neural networks Sustainable development Recommender systems

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Fruit Type Classification using Stacked Bi-directional Long Short Term Memory

Fruit Type Classification using Stacked Bi-directional Long ...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Subba Reddy Borra Venkata Ramana Kaneti Vontela Tejaswini S Anitha Vinaykumar R IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In recent years, Machine Learning (ML) and Deep Learning (DL) applications have become increasingly important in agriculture, particularly in the classification of fruits based on image data. Fruit recognition and categorization have attained a lot of attention from researchers. Because of the diversity and heterogeneity of fruits, this is a difficult challenge. To solve this, a Stacked Bi-LSTM image classification approach has been proposed to enhance the accuracy of fruit image classification. The process includes feature extraction by a Convolutional Neural Network (CNN), followed by feature selection using a Recurrent Neural Network (RNN). Following that, the proposed Stacked Bi-LSTM model is utilized to classify fruit images, resulting in enhanced accuracy. As indicated by evaluation parameters like as recall, precision, and F-measure, the acquired results outperform previous algorithms. The analyzed outcomes show that the Stacked Bi-LSTM model accomplishes precision of 85.4%, recall of 88.3%, and F1-score of 83.9%, demonstrating greater performance when compared to existing approaches.

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An Improved Ant Lion Optimization Algorithm Based Convolutional Neural Network for Big data Classification

An Improved Ant Lion Optimization Algorithm Based Convolutio...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： P. Umaeswari J. Vellingiri Gotte Ranjith Kumar Devendhiran A Ashitha V Naik Department of Computer Science and Business Systems R.M.K. Engineering College Kavaraipettai India School of Computer Science Engineering and Information Systems Institute of Technology Vellore India School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science and Technology Vivekanandha College of Engineering for Women Namakkal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

The continuous advancement of remote sensor technology is contributing to a daily surge in data production, necessitating improvements in the accuracy of big data classification. This research proposes a unique feature selection method named as Improved Ant Lion Optimization (IALO) algorithm that integrates the behavior of animal migration with Ant Lion Optimization (ALO) algorithm. The proposed feature selection method enhances the performance of the ALO algorithm. The datasets utilized for the research are iris, wine, Cleveland, and Switzerland datasets. Next, the map reduces framework is utilized and then feature extraction is processed by Linear Discriminant Analysis (LDA). The feature selection is performed by the proposed IALO algorithm and the selected features are given to the Convolutional Neural Network (CNN) classifier for the big data classification. The performance of the proposed method is evaluated by parameters such as accuracy, computation time, specificity, and sensitivity. The proposed method attained a high accuracy of 98.05% with less computation time of 4.8 secs in the wine dataset which is comparatively higher than other existing methods such as Grey Wolf Optimization based CNN (GWO-CNN), Particle Swarm Optimization based CNN (PSO-CNN), Cuckoo Search Optimization based CNN (CSO-CNN), Ant Lion Optimization based CNN (ALO-CNN).

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Attention Gate with Bidirectional Long Short-Memory for Crop Type Classification Using Multi-Spectral Satellite Images

Attention Gate with Bidirectional Long Short-Memory for Crop...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Alugula Damodar Rao K Sudheer Kumar Ramya Srikanteswara Muntather Almusawi A. H. A. Hussein Malla Reddy Engineering Collegw for Women Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The satellite sensors are used for the monitoring of the crops based on an automated crop detection framework. The Attention Gate with Bidirectional Long Short-Memory (AG-BiLSTM) is developed for crop type classification using multi-spectral satellite images. The min-max normalization is used for preprocessing which scales every feature into common scale because satellite images have different ranges thereby leading different scales of features. The Principal Component Analysis (PCA) is used for feature extraction which minimizes the data dimensionality that retains required features when minimizing redundancy and noise. It leads enhanced computational effectiveness and performance through concentrates on significant data. The AG-BiLSTM enhance the capability to concentrate on relevant features which improves classification accuracy. The BiLSTM captures contextual data from both previous and future information which enhance the performance. AG-BiLSTM reached 98.61% of accuracy, 97.49% of precision, 96.28% of recall and 96.88% of f1-score for Sentinel-1 dataset compared to 3D Convolution Neural Network (3D-CNN).

关键词： Accuracy Recurrent neural networks Redundancy Noise Crops Sentinel-1 Feature extraction Satellite images Long short term memory Principal component analysis

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An Effective Expectation of Diabetes Mellitus via Improved Support Vector Machine through Cloud Security

An Effective Expectation of Diabetes Mellitus via Improved S...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Dayanand Lal N Hema K Spoorthi T Ramesh Chegoni Madhura G K Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of IT CMR Technical Campus Kandlakoya India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

Nerve pain, eye difficulties, skin issues, renal problems, heart disease, depression, and a host of other illnesses are all linked to diabetes if left untreated. In an effort to lengthen the human lifespan, scientists all around the world have applied a variety of methods for early disease prediction. Diseases of the eyes, heart, and kidneys are prioritised because they can cause serious consequences and even death if left untreated. After conducting an in-depth analysis of the strategies used by several researchers, the following models were constructed and tested using the datasets most frequently favoured by leading scientists throughout the world. Diabetic retinopathy (DR) is an eye condition, and this research seeks to construct a hybrid learning model for early prediction of DR. The proposed approach combines elements of both Machine Learning and Deep Learning. Score Level Fusion is the last arbiter of the model's precision. Images from the first dataset of its sort reflecting the Indian population, the Indian Diabetic Retinopathy Imaging Dataset, were used to make the prediction. Predicting the DR and recommending the necessary treatment to patients is possible based on severity levels, which ultimately leads to the elimination of blindness.

关键词： Support vector machines Deep learning Heart Retinopathy Blindness Predictive models Retina

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Improved COOT Bird Optimization for Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network

Improved COOT Bird Optimization for Efficient Clustering and...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Ahmad Alkhayyat Gotte Ranjith Kumar R Pramodhini R Rana Veer Samara Sihman Bharattej Karanam Madhavi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science &Artificial Intelligence SR University Warangal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Saint Leo University St Leo USA Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In the domain of IoT-assisted Wireless Sensor Networks (WSN), the significance of efficient clustering and routing protocols cannot be overstated, as they play a crucial role in enhancing data access mechanisms and mitigating reliability issues. While existing methods aim to tackle these challenges, with complexities in implementation and dependencies on the real-world effectiveness of optimization algorithms. Another pivotal concern revolves around augmenting energy efficiency in diverse IoT environments, prompting researchers to explore innovative techniques. This abstract introduces the Improved COOT Bird Optimization for Metaheuristic-Based Clustering and Routing Algorithm (ICOOT) as a promising solution. ICOOT exhibits superior performance metrics, boasting a Residual Energy of 1.992 J, a Packet Delivery Ratio (PDR) of 9.67, a Network Lifetime Analysis of 18,987 rounds, and a Throughput of 0.87 kbps. Comparative analysis with established methods like CBR-ICWSN, TSGWO, iASEF, and EENFC-MRP highlights ICOOT’s potential in effectively addressing the challenges inherent in clustering and routing within IoT-assisted WSN.

关键词： Wireless sensor networks Heuristic algorithms Clustering algorithms Routing Throughput Birds Routing protocols Complexity theory Reliability Optimization

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High-Spatial-Resolution Remote Sensing Scene Method Based on Spatial Adaptive Convolution

High-Spatial-Resolution Remote Sensing Scene Method Based on...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Ayub Baig Bura Vijay Kumar K. Arun Maitrey Bhookya Ramesh Karunakara Rai B IT Department Malla Reddy Engineering College for Women Hyderabad India School of Computer Science and Artificial Intelligence SR University Warangal India Computer Science and Engineering (Data Science) Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The High-Spatial-Resolution (HSR) remote sensing is categorized through its ability to generate fine-grained spatial data. The image resolution is typically calculated through Ground Sample Distance (GSD) that presents the smallest ground object size that can be solved in an images. This paper presents a remote sensing image semantic segmentation for resolving issues through Hierarchical Atrous Pyramid (HASP) and Spatial Adaptive Network (SPANet) based framework. The HASP is utilized to resolve the issues of difficult background and high-scale variances through expanding the receptive domain of network. The SPANet has a capability to adjust convolutional operations adaptively that reduces the computational load. It can lead to quicker inference time and make it is feasible for real-time applications. The loss function is calculated which assists network to enhance the segmentation result. The parameters like accuracy, Intersection over Union (IoU), mean IoU (mIoU) and mean F1 (mF1) are utilized to estimating model performance on iSAID dataset. The SPANet obtains high accuracy of 98.74%, IoU of 84.68%, mIoU of 72.26% and mF1 of 81.59% which is comparatively higher than existing methods like Foreground-Aware Relation Network (FarSeg++) and Cascade Class-Aware Enhanced Network (CCENet).

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Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization

Utilizing Machine Learning for Sport Data Analytics in Crick...

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Mysore Sub Section International Conference (MysuruCon), IEEE

作者： R Suguna Y Praveen Kumar J Suriya Prakash P.S Neethu S Kiran Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Vidhya Jyothi Institute of Technology Hyderabad India Department of Computer Science and Engineering JAIN (Deemed-to-be-University) Bengaluru India School of Engineering and Technology CHRIST (Deemed to be University) Bengaluru India Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore India

Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket.

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Improved Welding Defects Recognition with Transfer Learning Based You Only Look Once Version 5

Improved Welding Defects Recognition with Transfer Learning ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Pavan Srikanth SubbaRaju Patchamatla Raami Riadhusin N. Subhash Chandra Pramodhini R K. Alagarraja Patchamatla RKInfo-Tech LLC Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, welding is a dynamic joining procedure for infrastructure development and energy production to ensure safety and reliability. However, weld defects often reduce the quality of welded parts. Existing TL-CNN models struggled with variations in defect appearance, leading to biased training and poor generalization. To overcome this issue, TL based You Only Look Once version 5 (TL-YOLOv5) is proposed. The pretrained YOLOv5 model is fine tuned for optimizing the welding data to generalize varied appearances. Initially, the input data is collected from asymmetric laser weld images and then preprocessed with median filter for image denoising and then enhanced with gray scale transformation. After that, Convolutional Neural Network (CNN) with improved activation function and adaptive pooling is incorporated to extract features from the preprocessed images. Finally, the input feature maps are further fed into the proposed TL-YOLOv5 for weld defect recognition with anchor boxes and fine-tuned to improve performance. From the results, the proposed TL-YOLOv5 attained better results in contrast to conventional TL-CNN including accuracy (99.12%) respectively.

关键词： YOLO Welding Transfer learning Data preprocessing Feature extraction Data models Convolutional neural networks Integrated circuit modeling Image enhancement Image denoising

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Modified Salp Swarm Optimization for Mobile Secure Routing in Intrusion Detection System for Wireless Sensor Network

Modified Salp Swarm Optimization for Mobile Secure Routing i...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Layth Hussein K Sudheer Kumar S Trisheela R Rana Veer Samara Sihman Bharattej Jajimoggala Sravanthi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Saint Leo University St Leo USA Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Mobile secure routing is crucial for protecting wireless networks from potential threats and ensuring the confidentiality of transmitted data. Challenges such as dynamic topology changes and susceptibility to attacks underscore the need for robust algorithms. This paper introduces the Modified Salp Swarm Algorithm (MSSA) to optimize mobile secure routing within an Intrusion Detection System (IDS) for Wireless Sensor Networks (WSNs). MSSA, leveraging swarm intelligence, enhances network resilience through dynamic path optimization, effectively countering security risks. Numerical validation attests to MSSA’s, achieving an energy consumption of 0.08 J and 12% of average end-to-end delay, 89 kbps of throughput, and 95% of detection rate. Comparative analysis against established methods such as Ant Colony Optimization (ACO), Mobile Agent-based Dynamic Intrusion Tracing (MADIT), Bacterial Foraging Optimization Algorithm (BFOA), and Trust-Based Secure and Energy-Efficient Routing (TBSEER) consistently positions MSSA as the superior solution, underscoring its effectiveness in addressing the complexities of mobile secure routing in wireless networks.

关键词： Wireless sensor networks Sensitivity Network topology Heuristic algorithms Wireless networks Routing Throughput Security Particle swarm optimization Optimization

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